mobile feature-cloud panorama construction for image recognition applications
DESCRIPTION
Mobile feature-cloud panorama construction for image recognition applications Miguel Bordallo, Jari Hannuksela, Olli silvén Machine Vision Group University of Oulu. Contents. Introduction Image recognition applications Comparison of image-based context retrieval methods - PowerPoint PPT PresentationTRANSCRIPT
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MACHINE VISION GROUP
MOBILE FEATURE-CLOUD PANORAMA CONSTRUCTION FOR IMAGE
RECOGNITION APPLICATIONS
Miguel Bordallo, Jari Hannuksela, Olli silvénMachine Vision Group
University of Oulu
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MACHINE VISION GROUP
Contents
• Introduction• Image recognition applications
– Comparison of image-based context retrieval methods
• Context retrieval from video analysis• System design
– Application flow– Automatic start– Image registration– Moving-objects detection– Quality assesment
• Performance analysis• Conclusions
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MACHINE VISION GROUP
Image-based context retrieval applications
•Point Your Camera to an object (landmark, poster)•Take a Picture•Get context information and display it
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MACHINE VISION GROUP
Mobile context retrieval applications
Google googles
Snaptell
Kooaba
Nokia Point & Find
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MACHINE VISION GROUP
Image recognition approaches
• Videos contain lots of information– Most of it redundant
• Image registration is easy – Smaller motions between frames– some frames can be discarded without
losing information
• Videos can capture wide angle scenes. – 3D world is better represented
• Transmission of compressed still image• Needs lots of storage in server• Image size implies large amount of data transmitted• Compression artifacts diminish quality
• Features extracted from still images• Amount of features needed not know beforehand• No feedback. Re-takes needed often• Two dimensional representation
• Feature-cloud extracted from video frames
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MACHINE VISION GROUP
Still image vs. Video based
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MACHINE VISION GROUP
Constructing a feature-cloud
Frame #1 Frame #16 Frame #31 Frame #46 Frame #61
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MACHINE VISION GROUP
System design
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MACHINE VISION GROUP
Application flow (client side)
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MACHINE VISION GROUP
Automatic start of the application
•Recognizing characteristic motion patterns • Holding phone like a camera• Panning back and forth
•Reduces perceived latencies
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MACHINE VISION GROUP
Interactive capture
VGA video analysis
Motion estimation system calculates shift, rotation and scale in real time
When frame is suitable for recognition (high quality), the user receives feedback and instructions
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MACHINE VISION GROUP
Feature extraction & Image registration
• Feature extraction based on CHoG features• Compressed Histogram of Gradients
• Block Matching
• Best Linear Unbiased Estimator
• Compute registration parameters in real time to send to the server:
•Shift, rotation and change of scale
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MACHINE VISION GROUP
Moving-objects detection
Object-detection ON Object-detection OFF
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MACHINE VISION GROUP
Moving-objects detection
The features corresponding to a moving object are not sent to the server
Not-valid features are transmitted tothe server
Object-detection ON Object-detection OFF
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MACHINE VISION GROUP
Quality assesment
Server receives only the features corresponding to high quality frames
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MACHINE VISION GROUP
Performance comparison
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MACHINE VISION GROUP
Summary
• Improve results in 3 dimensional environments• Interactivity• Detection of moving objects• Image quality assesment• Bigger field of view
• Reduce the communications need between clients and server
•Bandwidth reduction
• Reduce the workload of the servers